Lectures in Pattern Theory: Volume 3: Regular Structures - Search

lgli/Cs_Computer science/CsIp_Image processing/Grenander U. General pattern theory.. a mathematical study of regular structures (OUP, 1994)(ISBN 0198536712)(600dpi)(T)(924s)_CsIp_.djvu

General Pattern Theory: A Mathematical Study of Regular Structures (Oxford Mathematical Monographs) Ulf Grenander Clarendon; Oxford University Press; Clarendon Press, Oxford Mathematical Monographs, 1994

The aim of pattern theory is to create mathematical knowledge representations of complex systems, analyze the mathematical properties of the resulting regular structures, and to apply them to practically occurring patterns in nature and the man-made world. Starting from an algebraic formulation of such representations they are studied in terms of their topological, dynamical and probabilistic aspects. Patterns are expressed through their typical behavior as well as through their variability around their typical form. Employing the representations (regular structures) algorithms are derived for the understanding, recognition, and restoration of observed patterns. The algorithms are investigated through computer experiments. The book is intended for statisticians and mathematicians with an interest in image analysis and pattern theory.

English [en] · DJVU · 11.7MB · 1994 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/General pattern theory: a mathematical study of regular structures/70d654e7af39dbe15c614bf757cfb4e6.djvu

General Pattern Theory: A Mathematical Study of Regular Structures (Oxford Mathematical Monographs) Ulf Grenander Oxford University Press, USA, Oxford Mathematical Monographs, 1994

The aim of pattern theory is to create mathematical knowledge representations of complex systems, analyze the mathematical properties of the resulting regular structures, and to apply them to practically occurring patterns in nature and the man-made world. Starting from an algebraic formulation of such representations they are studied in terms of their topological, dynamical and probabilistic aspects. Patterns are expressed through their typical behavior as well as through their variability around their typical form. Employing the representations (regular structures) algorithms are derived for the understanding, recognition, and restoration of observed patterns. The algorithms are investigated through computer experiments. The book is intended for statisticians and mathematicians with an interest in image analysis and pattern theory.

English [en] · DJVU · 13.7MB · 1994 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/All About Regular Expressions/d0f78085d396352966fc8611f6f1c560.pdf

All About Regular Expressions Jan Goyvaerts https://www.regular-expressions.info/, 2019, 2019

A regular expression (regex or regexp for short) is a special text string for describing a search pattern. You can think of regular expressions as wildcards on steroids. You are probably familiar with wildcard notations such as \*.txt to find all text files in a file manager. The regex equivalent is ^.\*\.txt$. This tutorial is quite unique because it not only explains the regex syntax, but also describes in detail how the regex engine actually goes about its work. You will learn quite a lot, even if you have already been using regular expressions for some time. This will help you to understand quickly why a particular regex does not do what you initially expected, saving you lots of guesswork and head scratching when writing more complex regexes. There are many software applications and programming languages that support regular expressions. If you are a programmer, you can save yourself lots of time and effort. You can often accomplish with a single regular expression in one or a few lines of code what would otherwise take dozens or hundreds. Many applications and programming languages have their own implementation of regular expressions, often with slight and sometimes with significant differences from other implementations. When two applications use a different implementation of regular expressions, we say that they use different “regular expression flavors”. Unlike most other regex tutorials, this tutorial covers all the popular regular expression flavors, and indicates the differences that you should watch out for.

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lgli/Cs_Computer science/CsIp_Image processing/Grenander U. Lectures in pattern theory 2.. Pattern analysis (AMS024, Springer, 1978)(ISBN 0387903100)(600dpi)(K)(T)(O)(613s)_CsIp_.djvu

Lectures in Pattern Theory : Volume 2: Pattern Analysis Grenander U. Springer New York, Applied Mathematical Sciences 024, 1978

Many persons have helped the author with comments and corrections, and I would like to mention D. E. McClure, I. Frolow, J. Silverstein, D. Town, and especially W. Freiberger for his helpful suggestions and encouragement. The work in Chapters 6 and 7 has been influenced and stimulated by discussions with other members of the Center for Neural Sciences, especially with L. Cooper and H. Kucera. I would like to thank F. John, J. P. LaSalle, L. Sirovich, and G. Whitham for accepting the manuscript for the series Applied Mathematical Sciences published by Springer-Verlag. This research project has been supported by the Division of Mathematical and Computer Sciences of the National Science Foundation and (the work on language abduction, pattern processors, and patterns in program behavior) by the Information Systems Program of the Office of Naval Research. I greatly appreciate the understanding and positive interest shown by John Pasta, Kent Curtiss, Bruce Barnes, Sally Sedelov vi PREFACE and Bob Agins of the Foundation, and by Marvin Denicoff of the Office of Naval Research. I am indebted to Mrs. E. Fonseca for her untiring and careful preparation of the manuscript, to Miss E. Addison for her skillful help with the many diagrams, and to S.V. Spinacci for the final typing. I gratefully acknowledge permission to reproduce figures, as mentioned in the text, from Cambridge University Press and from Hayden Book Company. Also, to Professor J. Carbury for permission to use his illustration on page 704. Erscheinungsdatum: 21.02.1978

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nexusstc/Pattern Analysis: Lectures in Pattern Theory Volume II/241f590520085f6513b284853d8fef57.djvu

Pattern Analysis : Lectures in Pattern Theory Volume II U. Grenander (auth.) Springer-Verlag New York, Applied Mathematical Sciences, Applied Mathematical Sciences 24, 1, 1978

Many persons have helped the author with comments and corrections, and I would like to mention D. E. McClure, I. Frolow, J. Silverstein, D. Town, and especially W. Freiberger for his helpful suggestions and encouragement. The work in Chapters 6 and 7 has been influenced and stimulated by discussions with other members of the Center for Neural Sciences, especially with L. Cooper and H. Kucera. I would like to thank F. John, J. P. LaSalle, L. Sirovich, and G. Whitham for accepting the manuscript for the series Applied Mathematical Sciences published by Springer-Verlag. This research project has been supported by the Division of Mathematical and Computer Sciences of the National Science Foundation and (the work on language abduction, pattern processors, and patterns in program behavior) by the Information Systems Program of the Office of Naval Research. I greatly appreciate the understanding and positive interest shown by John Pasta, Kent Curtiss, Bruce Barnes, Sally Sedelov vi PREFACE and Bob Agins of the Foundation, and by Marvin Denicoff of the Office of Naval Research. I am indebted to Mrs. E. Fonseca for her untiring and careful preparation of the manuscript, to Miss E. Addison for her skillful help with the many diagrams, and to S.V. Spinacci for the final typing. I gratefully acknowledge permission to reproduce figures, as mentioned in the text, from Cambridge University Press and from Hayden Book Company. Also, to Professor J. Carbury for permission to use his illustration on page 704. Erscheinungsdatum: 21.02.1978

English [en] · DJVU · 4.5MB · 1978 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

duxiu/initial_release/a_40352952.zip

Lectures in Pattern Theory, Volume II: Pattern Analysis Ulf Grenander Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg, Applied Mathematical Sciences, 1978, 1976

Many persons have helped the author with comments and corrections, and I would like to mention D. E. McClure, I. Frolow, J. Silverstein, D. Town, and especially W. Freiberger for his helpful suggestions and encouragement. The work in Chapters 6 and 7 has been influenced and stimulated by discussions with other members of the Center for Neural Sciences, especially with L. Cooper and H. Kucera. I would like to thank F. John, J. P. LaSalle, L. Sirovich, and G. Whitham for accepting the manuscript for the series Applied Mathematical Sciences published by Springer-Verlag. This research project has been supported by the Division of Mathematical and Computer Sciences of the National Science Foundation and (the work on language abduction, pattern processors, and patterns in program behavior) by the Information Systems Program of the Office of Naval Research. I greatly appreciate the understanding and positive interest shown by John Pasta, Kent Curtiss, Bruce Barnes, Sally Sedelov vi PREFACE and Bob Agins of the Foundation, and by Marvin Denicoff of the Office of Naval Research. I am indebted to Mrs. E. Fonseca for her untiring and careful preparation of the manuscript, to Miss E. Addison for her skillful help with the many diagrams, and to S.V. Spinacci for the final typing. I gratefully acknowledge permission to reproduce figures, as mentioned in the text, from Cambridge University Press and from Hayden Book Company. Also, to Professor J. Carbury for permission to use his illustration on page 704. Erscheinungsdatum: 21.02.1978

English [en] · PDF · 93.4MB · 1976 · 📗 Book (unknown) · 🚀/duxiu/zlibzh · Save

nexusstc/Machine Learning in Modeling and Simulation: Methods and Applications/17ff698562f056687b473a5b2a978b93.epub

Machine Learning in Modeling and Simulation : Methods and Applications Timon Rabczuk; Klaus-Jürgen Bathe Springer International Publishing AG, Computational Methods in Engineering & the Sciences, 2023

Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering. Erscheinungsdatum: 04.10.2023

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nexusstc/Human-Centered Artificial Intelligence: Advanced Lectures/7f61bd1b1fc842d62013da4abdd68958.pdf

Human-Centered Artificial Intelligence : Advanced Lectures Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra Springer International Publishing AG, Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 13500, 2023

As a discipline, human-centered AI (HCAI) aims to create Artificial Intelligence (AI) systems that collaborate with humans, enhancing human capabilities and empowering humans to achieve their goals. That is, the focus amplify and augment rather than displace human abilities. HCAI seeks to preserve human control in a way that ensures artificial intelligence meets our needs while also operating transparently, delivering equitable outcomes, and respecting human rights and ethical standards. Design methods that enable representation of and adherence to values such as privacy protection, autonomy (human in control), and non-discrimination are core to HCAI. These are themes closely connected to some of the most fundamental challenges of AI. Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. This chapter provides an introduction to Artificial Neural Networks, with a review of the early history of perceptron learning. It presents a mathematical notation for multi-layer neural networks and shows how such networks can be iteratively trained by back-propagation of errors using labeled training data. It derives the back-propagation algorithm as a distributed form of gradient descent that can be scaled to train arbitrarily large networks given sufficient data and computing power. Black-box Artificial Intelligence (AI) systems for automated decision making are often based on over (big) human data, map a user’s features into a class or a score without exposing why. This is problematic for the lack of transparency and possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training data, leading to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. This requires good communication, trust, clarity, and understanding, like any efficient collaboration. Explainable AI (XAI) addresses such challenges, and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results. This chapter provides a reasoned introduction to the work of Explainable AI to date and surveys the literature focusing on symbolic AI-related approaches. We motivate the needs of XAI in real-world and large-scale applications while presenting state-of-the-art techniques and best practices and discussing the many open challenges. Artificial intelligence, and in particular Machine Learning methods, is fast gaining ground. Algorithms trained on large datasets and comprising numerous hidden layers, with up to a trillion parameters, are becoming common. Such models are difficult to explain to lay users with little understanding of the basis of machine learning, but they are also hard to interpret for those who designed and programmed them. The calculations that are carried out by the algorithm are not assigned an easily understandable meaning, aside from there being far too many of these calculations to actually follow. The outputs of algorithms are, as a result, hard to predict and to explain. Why did the algorithm output that there is a cat on this picture? We don’t really know, certainly not without additional help in the form of explainability tools. The first section, Introduction to Human-centered AI, presents the main definitions and concepts covered in this volume. The second section, Human-centered Machine Learning, includes several chapters on machine learning ranging from basic concepts of neural networks to interactive learning. This section also describes modern approaches such as transformers in natural language processing, speech processing, vision and multi-modal processing. The third section, Explainable AI, deals with both technical and philosophical concepts. The section includes a conceptual overview of computational cognitive vision together with practical demonstrations. The fourth section, ethics, law and society AI, introduces main concepts of Ethics and Law. This section also discusses ethics in communication. The fifth section, Argumentation, focuses on concepts of arguments and attacks. The concepts are illustrated with several concrete examples in cognitive technologies of learning and explainable inference or decision making. The last section, Social Simulation, deals with agent-based social simulations that are used to investigate complex phenomena within social systems. The chapters show how they could be designed, evaluated and employed by decision makers.

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lgli/Advances in Data Clustering Theory and Applications (Fadi Dornaika, Denis Hamad, Joseph Constantin etc.).pdf

ADVANCES IN DATA CLUSTERING : theory and applications Fadi Dornaika, Denis Hamad, Joseph Constantin, Truong Hoang Vinh SPRINGER NATURE, 1, 2024

Clustering, a foundational technique in data analytics, finds diverse applications across scientific, technical, and business domains. Within the theme of “Data Clustering,” this book assumes substantial importance due to its indispensable clustering role in various contexts. As the era of online media facilitates the rapid generation of large datasets, clustering emerges as a pivotal player in data mining and machine learning. At its core, clustering seeks to unveil heterogeneous groups within unlabeled data, representing a crucial unsupervised task in machine learning. The objective is to automatically assign labels to each unlabeled datum with minimal human intervention. Analyzing this data allows for categorization and drawing conclusions applicable across diverse application domains. The challenge with unlabeled data lies in defining a quantifiable goal to guide the model-building process, constituting the central theme of clustering. This book presents concepts and different methodologies of data clustering. For example, deep clustering of images, semi-supervised deep clustering, deep multi-view clustering, etc. This book can be used as a reference for researchers and postgraduate students in related research background.

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lgli/Practical Guide to Applied Conformal Prediction in Python .pdf

Practical Guide to Applied Conformal Prediction in Python : Learn and Apply the Best Uncertainty Frameworks to Your Industry Applications Valeriy Manokhin Packt Publishing, Limited, 2024

Valery Manokhin, Agus Sudjianto, "Practical Guide to Applied Conformal Prediction in Python: Learn and Apply the Best Uncertainty Frameworks to Your Industry Applications" Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction Book Description In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. "Practical Guide to Applied Conformal Prediction in Python" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications. Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, computer vision, and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification. This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers. Table of Contents Part 1: Introduction Chapter 1: Introducing Conformal Prediction Chapter 2: Overview of Conformal Prediction Part 2: Conformal Prediction Framework Chapter 3: Fundamentals of Conformal Prediction Chapter 4: Validity and Efficiency of Conformal Prediction Chapter 5: Types of Conformal Predictors Part 3: Applications of Conformal Prediction Chapter 6: Conformal Prediction for Classification Chapter 7: Conformal Prediction for Regression Chapter 8: Conformal Prediction for Time Series and Forecasting Chapter 9: Conformal Prediction for Computer Vision Chapter 10: Conformal Prediction for Natural Language Processing Part 4: Advanced Topics Chapter 11: Handling Imbalanced Data Chapter 12: Multi-Class Conformal Prediction Index Other Books You May Enjoy Authors Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.

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nexusstc/Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare/29ff7000bd42b9fb4909295a8535f041.pdf

Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare Mohamed Abdel-Basset, Ripon Kumar Chakrabortty, Abduallah Gamal CRC Press, CRC Press (Unlimited), Boca Raton, 2023

Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare, 1st Edition, is an excellent compilation of current and advanced Multi-Criteria Decision Making (MCDM) techniques and their applications to multiple recent and innovative healthcare analytics problems. The healthcare business has expanded rapidly in recent years, and one of the top priorities in the sector is now the efficacy and efficiency of the various healthcare delivery systems. The entire performance of hospitals must be improved if the healthcare business wants to see an improvement in both the satisfaction and safety of their patients. Finding the best medical facility among many of its competitors may be difficult since there are so many, and they are so highly diverse in terms of features and performance trade-offs. This book will be the first in the literature to bridge the Industrial Engineering perspectives and Computational Intelligence (CI) for different digitalized (aka, smart) environments. Cloud computing for electronic health (e-health) is a cutting-edge technology that has the potential to transform the healthcare sector completely. Cloud computing offers a multitude of benefits, including high speeds, flexibility, scalability, rapid deployment, resource sharing, energy savings, and cost- effective infrastructure, all of which have the potential to impact day-to-day living significantly. Most medical organizations are in the ideation stages of cloud computing services. Transitioning from traditional service models to cloud computing models is difficult for the healthcare sector. This book has brought together the introductory discussions, fundamental concepts, challenges, and insights of multiple advanced healthcare management problems along with the application of MCDM to obtain the best option among multiple alternatives. A few important takeaways from this book are: Developing an efficient model for supplier performance evaluation and selection in healthcare industries with incomplete information A computational reliance approach for assessing healthcare service quality aspects and their measurement in an uncertain environment An efficient and provable approach for recommending suitable mobile healthcare products under uncertain environments Establishing a decision-making strategy to select healthcare waste treatment methods Assessing the usability of mHealth applications in practice related to type 2 diabetes The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable MCDM technique when making decisions to prioritize the selection criteria of any healthcare-related problems to ensure a sustainable practice.

English [en] · PDF · 56.4MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2023/11/26/3031465865.pdf

Enterprise Design, Operations, and Computing : 27th International Conference, EDOC 2023, Groningen, The Netherlands, October 30-November 3, 2023, Proceedings 14367 Henderik A. Proper; Luise Pufahl; Dimka Karastoyanova; Marten van Sinderen; João Moreira Springer Nature Switzerland AG, Lecture Notes in Computer Science, Lecture Notes in Computer Science, 14367, 1, 2024

This book constitutes the refereed proceedings of the 27th International Conference on Enterprise Design, Operations, and Computing, EDOC 2023, held in Groningen, The Netherlands, during October 30–November 3, 2023. The 12 full papers included in this book were carefully reviewed and selected from 36submissions. They were organized in topical sections as follows: Enterprise Modeling, Enterprise Architecture & Engineering, Model-Based Software Engineering, Enterprise Analysis with Process Mining, Process Improvement & Engineering, and Modeling in an Enterprise Context.

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nexusstc/Computing and Combinatorics: 28th International Conference, COCOON 2022, Shenzhen, China, October 22–24, 2022, Proceedings/5179eae8e261b3295e08ef9a976a0d4e.epub

Computing and Combinatorics : 28th International Conference, COCOON 2022, Shenzhen, China, October 22–24, 2022, Proceedings Yong Zhang; Dongjing Miao; Rolf Möhring; (eds.) Springer International Publishing AG, Lecture Notes in Computer Science, Lecture Notes in Computer Science, 1, 2023

This book constitutes the proceedings of the 28th International Conference on Computing and Combinatorics, COCOON 2022, held in Shenzhen, China, in October 2022. The 39 full papers together with 12 short papers presented in this volume were carefully reviewed and selected from 101 submissions. The papers focus on subjects such as Algorithmica, Theoretical Computer Science, Journal of Combinatorial Optimization and others

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lgli/Cs_Computer science/CsLn_Lecture notes/C/Combinatorial Pattern Matching, 10 conf., CPM 99(LNCS1645, Springer, 1999)(ISBN 3540662782)(302s).pdf

Combinatorial Pattern Matching: 10th Annual Symposium, Cpm 99, Warwick University, Uk, July 22-24, 1999 Proceedings (lecture Notes In Computer Science) Takuya Kida, Masayuki Takeda, Ayumi Shinohara (auth.), Maxime Crochemore, Mike Paterson (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science, Lecture Notes in Computer Science 1645, 1, 1999

Annotation This book constitutes the refereed proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching, CPM 99, held in Warwick, UK in July 1999. The 21 revised papers presented were carefully reviewed and selected from 26 submissions. The papers address all current issues in combinatorial pattern matching dealing with a variety of classical objects like trees, regular expressions, graphs, point sets, and arrays as well as with DNA/RNA coding, WWW issues, information retrieval, data compression, and pattern recognition

English [en] · PDF · 5.4MB · 1999 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

nexusstc/Reversible Data Hiding in Encrypted Images Based on Image Reprocessing and Polymorphic Compression/136817602e2d573c19d834d833067bfc.pdf

Reversible Data Hiding in Encrypted Images Based on Image Reprocessing and Polymorphic Compression Yicheng Zou; Yaling Zhang; Chao Wang; Tao Zhang; Yu Zhang Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Communications in Computer and Information Science, 2023

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

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nexusstc/Advances in Graph Neural Networks/2391f751b47f88ab294b645c06285d52.pdf

Advances in Graph Neural Networks Chuan Shi, Xiao Wang, Cheng Yang Springer International Publishing Springer, Synthesis Lectures on Data Mining and Knowledge Discovery, Synthesis Lectures on Data Mining and Knowledge Discovery, 1, 2023

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications. Erscheinungsdatum: 17.11.2022

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lgli/R:\062020\springer2\10.1007%2F978-3-642-14197-3.pdf

Conceptual structures : from information to intelligence : 18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010 : proceedings Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, Dickson Lukose (eds.) Springer Berlin Heidelberg : Imprint: Springer, 10.1007/97, 2010

th The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme “From Information to Intelligence” hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings.

English [en] · PDF · 4.2MB · 2010 · 📘 Book (non-fiction) · 🚀/lgli/scihub/zlib · Save

scihub/10.1007/978-3-7908-1850-5.pdf

[Studies in Fuzziness and Soft Computing] Fuzzy Classifier Design Volume 49 || Kuncheva, Ludmila I. Physica-Verlag HD : Imprint : Physica, 10.1007/97, 2000

Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever since have been a center of many discussions, fervently admired and condemned. Both proponents and opponents consider the argu ments pointless because none of them would step back from their territory. And stiH, discussions burst out from a single sparkle like a conference pa per or a message on some fuzzy-mail newsgroup. Here is an excerpt from an e-mail messagepostedin1993tofuzzy-mail@vexpert. dbai. twvien. ac. at. by somebody who signed "Dave". , . . . Why then the "logic" in "fuzzy logic"? I don't think anyone has successfully used fuzzy sets for logical inference, nor do I think anyone wiH. In my admittedly neophyte opinion, "fuzzy logic" is a misnomer, an oxymoron. (1 would be delighted to be proven wrong on that. ) . . . I carne to the fuzzy literature with an open mind (and open wal let), high hopes and keen interest. I am very much disiHusioned with "fuzzy" per se, but I did happen across some extremely interesting things along the way. " Dave, thanks for the nice quote! Enthusiastic on the surface, are not many of us suspicious deep down? In some books and journals the word fuzzy is religiously avoided: fuzzy set theory is viewed as a second-hand cheap trick whose aim is nothing else but to devalue good classical theories and open up the way to lazy ignorants and newcomers.

English [en] · PDF · 1.2MB · 2000 · 📘 Book (non-fiction) · 🚀/lgli/scihub/zlib · Save

lgli/A:\compressed\10.1007%2F978-94-009-1716-3.pdf

Integration of Natural Language and Vision Processing : Recent Advances Volume IV Paul Mc Kevitt (auth.), Paul Mc Kevitt (eds.) Springer Netherlands, 1st ed. 1996, The Hague, 1996

Although there has been much progress in developing theories, models and systems in the areas of Natural Language Processing (NLP) and Vision Processing (VP) there has up to now been little progress on integrating these two subareas of Artificial Intelligence (AI). This book contains a set of edited papers on recent advances in the theories, computational models and systems of the integration of NLP and VP. The volume includes original work of notable researchers: __Alex Waibel__ outlines multimodal interfaces including studies in speech, gesture and points; eye-gaze, lip motion and facial expression; hand writing, face recognition, face tracking and sound localization in a connectionist framework. __Antony Cohen__ and __John Gooday__ use spatial relations to describe visual languages. __Naoguki Okada__ considers intentions of agents in visual environments. In addition to these studies, the volume includes many recent advances from North America, Europe and Asia demonstrating the fact that integration of Natural Language Processing and Vision is truly an international challenge.

English [en] · PDF · 7.1MB · 1996 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/lncs1-ready\Croitoru M., Ferre S., Lukose D. (eds.) Conceptual Structures.. From Information to Intelligence (Springer, 2010)(ISBN 364214196X)(O)(218s).pdf

Conceptual structures : from information to intelligence : 18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010 : proceedings Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, Dickson Lukose (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science, Lecture Notes in Computer Science 6208 : Lecture Notes in Artificial Intelligence, 1, 2010

th The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme “From Information to Intelligence” hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings. Erscheinungsdatum: 07.07.2010

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upload/newsarch_ebooks_2025_10/2023/04/18/Artificial Intelligence in Control and Decision-making Systems.epub

Artificial Intelligence in Control and Decision-making Systems : Dedicated to Professor Janusz Kacprzyk Yuriy P. Kondratenko, Vladik Kreinovich, Witold Pedrycz, Arkadii Chikrii, Anna María Gil Lafuente Springer International Publishing, Studies in Computational Intelligence, Studies in Computational Intelligence, Volume 1087, 2023

This book presents an authoritative collection of contributions reporting on computational intelligence, fuzzy systems as well as artificial intelligence techniques for modeling, optimization, control and decision-making together with applications and case studies in engineering, management and economic sciences. Dedicated to the Academician of the Polish Academy of Sciences, Professor Janusz Kacprzyk in recognition of his pioneering work, the book reports on theories, methods and new challenges in artificial intelligence, thus offering not only a timely reference guide but also a source of new ideas and inspirations for graduate students and researchers alike. The book consists of the 18 chapters, presented by distinguished and experienced authors from 16 different countries (Australia, Brazil, Canada, Chile, Germany, Hungary, Israel, Italy, China, R.N.Macedonia, Saudi Arabia, Spain, Turkey, United States, Ukraine, and Vietnam). All chapters are grouped into three parts: Computational Intelligence and Fuzzy Systems, Artificial Intelligence Techniques in Modelling and Optimization, and Computational Intelligence in Control and Decision Support Processes. The book reflects recent developments and new directions in artificial intelligence, including computation method of the interval hull to solutions of interval and fuzzy interval linear systems, fuzzy-Petri-networks in supervisory control of Markov processes in robotic systems, fuzzy approaches for linguistic data summaries, first-approximation analysis for choosing fuzzy or neural systems and type-1 or type-2 fuzzy sets, matrix resolving functions in game dynamic problems, evolving stacking neuro-fuzzy probabilistic networks and their combined learning in online pattern recognition tasks, structural optimization of fuzzy control and decision-making systems, neural and granular fuzzy adaptive modeling, state and action abstraction for search and reinforcement learning algorithms. Among the most successful and perspective implementations in practical areas of human activity are tentative algorithms for neurological disorders, human-centric question-answering system, OWA operators in pensions, evaluation of the perception of public safety through fuzzy and multi-criteria approach, a multicriteria hierarchical approach to investment location choice, intelligent traffic signal control and generative adversarial networks in cybersecurity.

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lgli/2796.pdf

New Frontiers in Artificial Intelligence: JSAI-isAI 2021 Workshops, JURISIN, LENLS18, SCIDOCA, Kansei-AI, AI-BIZ, Yokohama, Japan, November 13–15, ... (Lecture Notes in Artificial Intelligence) Katsutoshi Yada (editor), Yasufumi Takama (editor), Koji Mineshima (editor), Ken Satoh (editor) Springer International Publishing, Springer Nature, Cham, 2023

This book constitutes extended, revised, and selected papers from the 13th International Symposium on Artificial Intelligence supported by the Japanese Society for Artificial Intelligence, JSAI-isAI 2021, held online in November 2021. The 26 full papers were carefully selected from 86 submissions. The papers are organized in the volume according to the following workshops: 15th International Workshop on Juris-Informatics, JURISIN 2021; 18th Workshop on Logic and Engineering of Natural Language Semantics, LENLS 18, 5th International Workshop on SCIentific DOCument Analysis, SCI-DOCA 2021; Workshop on Artificial Affective (Kansei) Intelligence, KANSEI-AI 2021; 5th Workshop on Artificial Intelligence of and for Business, AI-Biz 2021.

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nexusstc/A cascadic multigrid algorithm for variational inequalities/68d334d530926a5d854ccb4d27d98345.pdf

A cascadic multigrid algorithm for variational inequalities Blum H., Braess D., Suttmeier F.T. Springer-Verlag; Springer Verlag; Springer Science and Business Media LLC (ISSN 1432-9360), Computing and Visualization in Science, #3-4, 7, pages 153-157, 2004 oct 01

When classical multigrid methods are applied to discretizations of variational inequalities, several complications are frequently encountered mainly due to the lack of simple feasible restriction operators. These difficulties vanish in the application of the cascadic version of the multigrid method which in this sense yields greater advantages than in the linear case. Furthermore, a cg-method is proposed as smoother and as solver on coarse meshes. The efficiency of the new algorithm is elucidated by test calculations for an obstacle problem and for a Signorini problem.

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scihub/10.1007/978-3-642-22688-5.pdf

Conceptual structures for discovering knowledge : 19th International Conference on Conceptual Structures, ICCS 2011, Derby, UK, July 25-29, 2011 : proceedings Frithjof Dau (auth.), Simon Andrews, Simon Polovina, Richard Hill, Babak Akhgar (eds.) Springer Berlin Heidelberg : Imprint: Springer, 10.1007/97, 2011

This book constitutes the proceedings of the 19th International Conference on Conceptual Structures, ICCS 2011, held in Derby, UK, in July 2011. The 18 full papers and 4 short papers presented together with 12 workshop papers were carefully reviewed and selected for inclusion in the book. The volume also contains 3 invited talks. ICCS focuses on the useful representation and analysis of conceptual knowledge with research and business applications. It advances the theory and practice in connecting the user's conceptual approach to problem solving with the formal structures that computer applications need to bring their productivity to bear. Conceptual structures (CS) represent a family of approaches that builds on the successes of artificial intelligence, business intelligence, computational linguistics, conceptual modelling, information and Web technologies, user modelling, and knowledge management. Two of the workshops contained in this volume cover CS and knowledge discovery in under-traversed domains and in task specific information retrieval. The third addresses CD in learning, teaching and assessment.

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nexusstc/Understanding Deep Learning: Application in Rare Event Prediction/8018903fd02089fa7f74940de7c989cc.pdf

Understanding Deep Learning: Application in Rare Event Prediction Chitta Ranjan Ph.D Independently published, 2023

"It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure. Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.

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nexusstc/Graph Drawing and Network Visualization: 31st International Symposium, GD 2023, Isola delle Femmine, Palermo, Italy, September 20–22, 2023, Revised Selected Papers, Part I/2441124dfd37991e0272916fd2e19552.pdf

Graph Drawing and Network Visualization : 31st International Symposium, GD 2023, Isola Delle Femmine, Palermo, Italy, September 20–22, 2023, Revised Selected Papers, Part I Michael A. Bekos; Markus Chimani Springer Nature Switzerland AG, Lecture Notes in Computer Science, 2023

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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nexusstc/Pattern Recognition In Speech And Language Processing/6d51979aa7e0ec45f54b2360a3c36abb.pdf

Pattern Recognition In Speech And Language Processing Crc Press, 2003

English [en] · PDF · 4.0MB · 2003 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Mathematical Basics of Motion and Deformation in Computer Graphics/0cb9460fdce2492b8eb8e843fdfe7813.pdf

Mathematical Basics of Motion and Deformation in Computer Graphics: Second Edition (Synthesis Lectures on Visual Computing: Computer Graphics, A) Ken Anjyo, Hiroyuki Ochiai, Brian A. Barsky Morgan & Claypool Publishers, Synthesis lectures on visual computing, #27, Second edition, Cham, Switzerland, 2017

This synthesis lecture presents an intuitive introduction to the mathematics of motion and deformation in computer graphics. Starting with familiar concepts in graphics, such as Euler angles, quaternions, and affine transformations, we illustrate that a mathematical theory behind these concepts enables us to develop the techniques for efficient/effective creation of computer animation. This book, therefore, serves as a good guidepost to mathematics (differential geometry and Lie theory) for students of geometric modeling and animation in computer graphics. Experienced developers and researchers will also benefit from this book, since it gives a comprehensive overview of mathematical approaches that are particularly useful in character modeling, deformation, and animation.

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lgli/D:\!Genesis\!!ForLG\1541894-Новая подборка книг по цифровой обработке сигналов, распознава\Pattern recognition in speech and language processing.rar

Pattern recognition in speech and language processing WU CHOU, BIING HWANG JUANG CRC Press, 2003

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upload/newsarch_ebooks_2025_10/2023/12/08/Implementing MLOps in the Enterprise - Yaron Haviv.epub

Implementing MLOps in the Enterprise: A Production-First Approach Yaron Haviv; Noah Gift O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2023

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: - Learn the MLOps process, including...

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nexusstc/Robotics, Vision and Control: Fundamental Algorithms in Python/1dcdf967c0679f0b257ddf653b0f9edc.pdf

Robotics, Vision and Control: Fundamental Algorithms in Python, 3rd Edition Peter Corke Springer International Publishing AG, Springer Tracts in Advanced Robotics, 146, 3, 2023

This textbook provides a comprehensive, but tutorial, introduction to robotics, computer vision, and control. It is written in a light but informative conversational style, weaving text, figures, mathematics, and lines of code into a narrative that covers robotics and computer vision―separately, and together as robotic vision. Over 1600 code examples show how complex problems can be decomposed and solved using just a few simple lines of code. This edition is based on Python and is accompanied by fully open-source Python-based Toolboxes for robotics and machine vision. The new Toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems on a broad range of computing platforms. For the beginning student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The first two editions of this book were based on MATLAB in conjunction with open-source MATLAB Toolboxes that are now thirty years old – that’s a long time for any piece of software. Much has happened in the last decade that motivate a change to the software foundations of the book, and that has led to two third editions: The version you are reading, is based on Python which is a popular open-source language with massive third party support. The old MATLAB Toolboxes have been redesigned and reimplemented in Python, taking advantage of popular open-source packages and resources to provide platform portability, fast browser-based 3D graphics, online documentation, fast numerical and symbolic operations, shareable and web-browseable notebooks all powered by GitHub and the open-source community. The computational foundation of this book is Python 3, a powerful and popular programming language that is likely to be familiar to students, researchers and hobbyists. The core functionality of Python can be extended with packages that can be downloaded, installed and then imported. Python, combined with popular packages such as NumPy, SciPy and Matplotlib provides a powerful interactive mathematical software environment that makes linear algebra, data analysis and high-quality graphics a breeze. Functionality for robotics and computer vision is provided by additional easily-installable packages.

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lgli/A:\compressed\10.1007%2F978-3-642-10973-7.pdf

Finite Geometric Structures and Their Applications : Lectures Given at a Summer School of the Centro Internazionale Matematico Estivo (C.I.M.E.) Held in Bressanone (Bolzano), Italy, June 18-27, 1972 R. C. Bose (auth.), Prof. A. Barlotti (eds.) Springer-Verlag Berlin Heidelberg, C.I.M.E. Summer Schools, 60, 1st ed. 2011, Berlin, Heidelberg, 2011

R.c. Bose: Graphs And Designs.- R.h. Bruck: Construction Problems In Finite Projective Spaces.- R.h.f. Denniston: Packings Of Pg(3,q).- J. Doyen: Recent Results On Steiner Triple Systems.- H. Lüneburg: Gruppen Und Endliche Projektive Ebenen.- J.a. Thas: 4-gonal Configurations.- H.p. Young: Affine Triple Systems. R.c. Bose: Graphs And Designs -- R.h. Bruck: Construction Problems In Finite Projective Spaces -- R.h.f. Denniston: Packings Of Pg(3,q) -- J. Doyen: Recent Results On Steiner Triple Systems -- H. Lüneburg: Gruppen Und Endliche Projektive Ebenen -- J.a. Thas: 4-gonal Configurations -- H.p. Young: Affine Triple Systems. A. Barlotti (ed.). Originally Published: Firenze: Cremonese, 1972.

English [en] · German [de] · PDF · 9.0MB · 2011 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!genesis\library.nu\c2\_313421.c2c5017c994caec1c7c0ff00d69e154a.pdf

Conceptual structures : from information to intelligence : 18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010 : proceedings Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, Dickson Lukose (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science, Lecture Notes in Computer Science 6208 : Lecture Notes in Artificial Intelligence, 1, 2010

th The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme “From Information to Intelligence” hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings. Erscheinungsdatum: 07.07.2010

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lgli/D:\HDD4\!genesis\SPR_NEW_2013-12\bok%3A978-3-540-48452-3.pdf

Combinatorial Pattern Matching: 10th Annual Symposium, Cpm 99, Warwick University, Uk, July 22-24, 1999 Proceedings (lecture Notes In Computer Science) Takuya Kida, Masayuki Takeda, Ayumi Shinohara (auth.), Maxime Crochemore, Mike Paterson (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science, Lecture Notes in Computer Science 1645, 1, 1999

Annotation This book constitutes the refereed proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching, CPM 99, held in Warwick, UK in July 1999. The 21 revised papers presented were carefully reviewed and selected from 26 submissions. The papers address all current issues in combinatorial pattern matching dealing with a variety of classical objects like trees, regular expressions, graphs, point sets, and arrays as well as with DNA/RNA coding, WWW issues, information retrieval, data compression, and pattern recognition

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lgli/3409.pdf

Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part XIV (Communications in Computer and Information Science) Biao Luo (editor), Long Cheng (editor), Zheng-Guang Wu (editor), Hongyi Li (editor), Chaojie Li (editor) Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Springer Nature, Singapore, 2023

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

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lgli/3365.pdf

Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part XI (Communications in Computer and Information Science) Biao Luo (editor), Long Cheng (editor), Zheng-Guang Wu (editor), Hongyi Li (editor), Chaojie Li (editor) Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Springer Nature, Singapore, 2023

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

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nexusstc/A Three-Stage Framework for Event-Event Relation Extraction with Large Language Model/efb2776d9297295a81c0551b49f61fd2.pdf

A Three-Stage Framework for Event-Event Relation Extraction with Large Language Model Feng Huang; Qiang Huang; YueTong Zhao; ZhiXiao Qi; BingKun Wang; YongFeng Huang; SongBin Li Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Communications in Computer and Information Science, 2023

Expanding the parameter count of a large language model (LLM) alone is insufficient to achieve satisfactory outcomes in natural language processing tasks, specifically event extraction (EE), event temporal relation extraction (ETRE), and event causal relation extraction (ECRE). To tackle these challenges, we propose a novel three-stage extraction framework (ThreeEERE) that integrates an improved automatic chain of thought prompting (Auto-CoT) with LLM and is tailored based on a golden rule to maximize event and relation extraction precision. The three stages include constructing examples in each category, federating local knowledge to extract relationships between events, and selecting the best answer. By following these stages, we can achieve our objective. Although supervised models dominate for these tasks, our experiments on three types of extraction tasks demonstrate that utilizing these three stages approach yields significant results in event extraction and event relation extraction, even surpassing some supervised model methods in the extraction task.

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lgli/NLP with Python 3 Books in 1 - From Beginner to Advanced The Future Frontier and Next-Gen Solutions.epub

NLP with Python: 3 Books in 1 - "From Beginner to Advanced: The Future Frontier and Next-Gen Solutions" Watson, Jerome Independently Published, 2023

Embark on a transformative journey through the intricate world of Natural Language Processing (NLP) with Python, as we unveil the secrets to mastering this cutting-edge field from the ground up! "From Beginner to Advanced: The Future Frontier and Next-Gen Solutions" is meticulously crafted to guide you through the ever-evolving landscape of NLP, ensuring you are well-equipped with the knowledge and skills to thrive in this dynamic domain. This comprehensive guide kicks off with the basics, making it accessible even if you are new to Python or NLP. Grasp the foundational concepts of text processing, dive into the intricacies of tokenization, and unlock the potential of sentiment analysis with hands-on examples and practical exercises. As you progress, you'll delve deeper into sophisticated topics, exploring the realms of machine translation, named entity recognition, and deep learning applications in NLP. Understand the inner workings of various Python libraries and frameworks such as NLTK, SpaCy, and TensorFlow, and learn how to harness their capabilities to transform raw text into valuable insights. But this book is more than just a technical manual. It’s a gateway to the future of NLP, providing you with a sneak peek into emerging trends and next-gen solutions. Discover how advancements in transformer models, like BERT and GPT, are revolutionizing the field, and gain insights into developing your own state-of-the-art NLP applications. Beyond the code, we delve into the ethical considerations of NLP, ensuring you are aware of the potential biases and implications of your models. Learn how to build fair and responsible NLP solutions, safeguarding the integrity of your applications. Whether you’re a student, data scientist, software developer, or simply an enthusiast eager to decipher the language of machines, "From Beginner to Advanced: The Future Frontier and Next-Gen Solutions" is your key to unlocking the full potential of NLP with Python. What’s Inside Comprehensive coverage of NLP fundamentals and advanced topics Hands-on examples and practical exercises in Python Exploration of next-gen solutions and emerging trends in NLP Ethical considerations and responsible AI practices in NLP Accessible learning path, from beginner to advanced levels Embark on this exhilarating journey through the realms of NLP with Python, and prepare yourself for the future of text analysis and language understanding. Your adventure into the next frontier of NLP starts here!

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lgli/3419.pdf

Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VIII (Communications in Computer and Information Science, 1962) Biao Luo (editor), Long Cheng (editor), Zheng-Guang Wu (editor), Hongyi Li (editor), Chaojie Li (editor) Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Springer Nature, Singapore, 2023

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

English [en] · PDF · 95.1MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save

nexusstc/Multi-granularity Deep Vulnerability Detection Using Graph Neural Networks/d1823e6f165df8db0747decd4c462567.pdf

Multi-granularity Deep Vulnerability Detection Using Graph Neural Networks Tengxiao Yang; Song Lian; Qiong Jia; Chengyu Hu; Shanqing Guo Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd, Communications in Computer and Information Science, 2023

The significance of vulnerability detection has grown increasingly crucial due to the escalating cybersecurity threats. Investigating automated vulnerability detection techniques to avoid high false positives and false negatives is an important issue in the current software security field. In recent years, there has been a substantial focus on deep learning-based vulnerability detectors, which have achieved remarkable success. To fill the gap in multi-granularity program representation, we propose MulGraVD, a deep learning-based vulnerability detector at the function level. MulGraVD captures the continuity and structure of the programming language by considering information at word, statement, basic block, and function granularity respectively. To overcome the constraint posed by hyperparameter layers in the information aggregation process of graph neural networks, MulGraVD serially passes information from coarse to fine granularity, which facilitates the mining of vulnerability patterns. Our experimental evaluation on FFMPeg+Qemu and ReVeal datasets shows that MulGraVD significantly outperforms existing state-of-the-art methods in terms of precision, recall, and F1 score, with an average improvement of 11.62% in precision, 27.69% in recall, and 19.71% in F1 score.

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